This is a Chinese-language educational companion to DeepLearning.AI's 'Agentic AI' course by Andrew Ng, maintained by the Datawhale community. The repository is a hybrid learning resource: ~60 translated markdown files across 5 modules (agentic workflow intro, reflection design patterns, tool use, practical building tips, highly autonomous agents) plus executable Jupyter notebooks and supporting Python modules that implement the lab exercises. It is not a production application or service — it is courseware with real, runnable agent code demonstrating current LLM agent patterns.
This repository is an educational tutorial for learning LangChain, LangGraph, and Deep Agents frameworks. It contains two tracks: 101 (fundamentals) and 201 (production patterns) with Jupyter notebooks and standalone agents runnable via `langgraph dev`. The code demonstrates multi-agent supervision, human-in-the-loop interrupts, middleware hooks, MCP tool integration, and a deep research agent with skills and memory. All agents use mock or simulated data (Chinook SQLite, Tavily search) and are designed for learning rather than production. The architecture centers on a centralized model configuration (`utils/models.py`) that supports multiple LLM providers, and reusable patterns like subgraph composition, dynamic prompts, and composite backends. While the code is educational and lacks production hardening (error handling, testing, security), it provides clear, well-commented examples of advanced agent patterns.
Agentspan is a comprehensive, multi-language agent orchestration platform built on top of Orkes Conductor. It provides SDKs in Python, TypeScript, Java, and C# for defining, running, and deploying AI agents with rich features: tools, guardrails, multi-agent strategies (handoff, sequential, parallel, router, swarm, plan-execute), streaming, human-in-the-loop, code execution in sandboxes, credential management, and scheduling. The architecture follows a server/client pattern where agent definitions compile to Conductor workflows, with a Java/Spring Boot server, a Go CLI with a Bubble Tea TUI, and a React/Material-UI web interface for visual workflow editing and execution monitoring. The repository is archived (read-only) as of August 2026, with its active development having merged into the Orkes Conductor repository. Despite being archived, the codebase represents a mature, well-crafted multi-agent runtime with extensive examples, comprehensive testing, and clean abstractions across all supported languages. The SDKs demonstrate consistent design patterns—agent composition via strategies, tool registration via decorators/annotations, guardrail chains with composable termination conditions, and thread-safe credential injection—making the codebase a valuable reference for building agent orchestration systems.
This repository is a comprehensive educational resource for learning and building AI agents using the LangGraph framework. It consists of numerous tutorials and examples organized by chapter, covering fundamental concepts such as state graphs and tool usage, as well as advanced patterns including Retrieval-Augmented Generation (RAG), multi-agent supervisor systems, and human-in-the-loop workflows. Each lesson is designed as a runnable script that demonstrates specific LangGraph integrations with various APIs (e.g., OpenAI, Tavily, OpenWeatherMap) and techniques like vector stores with Chroma. While the repository serves its purpose as a learning tool, it exhibits some maintenance challenges due to code duplication (e.g., multiple copies of display_graph.py across chapters) and contains at least one known flawed implementation (the audit data monitor in chapter13) that suffers from schema mismatches and inefficient API usage, as indicated by log files.